Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learning.

Abstract

Risk assessments for a pediatric population are often conducted across multiple stages. For example, clinicians may evaluate risks prenatally, at birth, and during WellChild visits. While predictions at later stages typically achieve higher accuracy, it is clinically desirable to make reliable risk assessments as early as possible. Therefore, this study focuses on enhancing prediction performance in early-stage risk assessments. Our solution, Borrowing From the Future (BFF), is a contrastive multi-modal framework that treats each time window as a distinct modality. In BFF, a model is trained on all available data throughout the time while conduct risk assessment using the up-to-time information. This contrastive framework allows the model to "borrow" informative signals from later stages (e.g., WellChild visits) to implicitly supervise the learning at earlier stages (e.g., prenatal/birth stages). We validate BFF on two real-world pediatric outcome prediction tasks, demonstrating consistent improvements in early risk assessment. The code is at https://github.com/scotsun/bff.